Searching for collective behavior in a large network of sensory neurons.

TitleSearching for collective behavior in a large network of sensory neurons.
Publication TypeJournal Article
Year of Publication2014
AuthorsTkačik, G, Marre, O, Amodei, D, Schneidman, E, Bialek, W, Berry, MJ
JournalPLoS Comput Biol
Volume10
Issue1
Paginatione1003408
Date Published2014 Jan
ISSN1553-7358
KeywordsAction Potentials, Animals, Computational Biology, Entropy, Fishes, Models, Neurological, Movement, Nerve Net, Probability, Retina, Sensory Receptor Cells, Urodela
Abstract

<p>Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such "K-pairwise" models--being systematic extensions of the previously used pairwise Ising models--provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population's capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction.</p>

DOI10.1371/journal.pcbi.1003408
Alternate JournalPLoS Comput. Biol.
PubMed ID24391485
PubMed Central IDPMC3879139
Grant ListP50 GM071508 / GM / NIGMS NIH HHS / United States
R01 EY14196 / EY / NEI NIH HHS / United States